System and method for memory augmented domain adaptation

ABSTRACT

A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

BACKGROUND

Embodiments of the present specification relate generally to machinelearning, and more particularly to systems and methods for memoryaugmented continuous learning to adapt to a domain.

As will be appreciated, traditional machine learning techniques requirea large dataset to “learn” via extensive training. Also, the machinelearning techniques are typically trained using a dataset correspondingto a source domain. However, most statistical methods including machinelearning techniques are known to perform rather poorly in rarelyencountered scenarios/domains. In particular, many machine learningtechniques including neural networks encounter the problem of domainadaptation and hence suffer from degradation of performance of a modelon samples associated with a different but related domain. Problems indomain adaptation are typically attributed to diversity in the samples.This diversity in the samples is encountered even in controlledenvironments like medical imaging where training samples differ due todifferences in equipment, demography, pathological conditions,protocol/operator variability, patients/subjects, and the like.Moreover, the problem with domain adaptation is further compounded bydifficulty in obtaining voluminous data in healthcare and otherregulated domains for training or retraining the models.

It is desirable that an algorithm/technique trained using datacorresponding to a source domain adapts to a new target domain using asfew samples as possible. Certain currently available solutions to theproblem of domain adaptation entail enabling systems to learn fromerrors during deployment. One example approach calls for correcting theobserved errors by retraining the existing technique with new samples.However, such approaches disadvantageously suffer from drawbacks. In oneexample, adapting the algorithm to the new target domain requires alarge number of samples corresponding to the target domain. Also, inanother example, neural networks suffer from a phenomenon known as“catastrophic forgetting.”

Moreover, some presently available techniques use Memory AugmentedNeural Networks (MANN) for remembering rare events. Certain othertechniques use a few-shot learning method for adapting quickly tochanges in either the domain or task through the meta-learning paradigm.However, most of these approaches disadvantageously rely onmeta-learning from many classes.

BRIEF DESCRIPTION

In accordance with aspects of the present specification, a system ispresented. The system includes an acquisition subsystem configured toobtain images corresponding to a target domain. Moreover, the systemincludes a processing subsystem in operative association with theacquisition subsystem and including a memory augmented domain adaptationplatform, where the memory augmented domain adaptation platform isconfigured to compute one or more features corresponding to an inputimage, where the input image corresponds to a target domain, identify aset of support images based on the one or more features corresponding tothe input image, where the set of support images corresponds to thetarget domain, augment an input to a machine-learnt model with a set offeatures, a set of masks, or both the set of features and the set ofmasks corresponding to the set of support images to adapt themachine-learnt model to the target domain, and generate an output basedat least on the set of features, the set of masks, or both the set offeatures and the set of masks corresponding to the set of supportimages. Additionally, the system includes an interface unit configuredto present the output for analysis.

In accordance with another aspect of the present specification, aprocessing system for adapting a machine-learnt model is presented. Theprocessing system includes a memory augmented domain adaptation platformconfigured to compute one or more features corresponding to an inputimage, where the input image corresponds to a target domain, identify aset of support images based on the one or more features corresponding tothe input image, where the set of support images corresponds to thetarget domain, augment an input to the machine-learnt model with a setof features, a set of masks, or both the set of features and the set ofmasks corresponding to the set of support images to adapt themachine-learnt model to the target domain, generate an output based atleast on the set of features, the set of masks, or both the set offeatures and the set of masks corresponding to the set of supportimages, and provide the output to facilitate analysis.

In accordance with yet another aspect of the present specification, amethod for adapting a machine-learnt model is presented. The methodincludes receiving an input image, where the input image corresponds toa target domain. Further, the method includes computing one or morefeatures corresponding to the input image. Moreover, the method includesidentifying a set of support images based on the one or more featurescorresponding to the input image, where the set of support imagescorresponds to the target domain. In addition, the method includesaugmenting an input to the machine-learnt model with a set of features,a set of masks, or both the set of features and the set of maskscorresponding to the set of support images to adapt the machine-learntmodel to the target domain The method also includes generating an outputbased at least on the set of features, the set of masks, or both the setof features and the set of masks corresponding to the set of supportimages. Furthermore, the method includes outputting the output tofacilitate analysis.

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic representation of an exemplary system for memoryaugmented domain adaptation, in accordance with aspects of the presentspecification;

FIG. 2 is a flow chart of an exemplary method for memory augmenteddomain adaptation, in accordance with aspects of the presentspecification;

FIG. 3 is a schematic representation illustrating an exemplary methodfor memory augmented domain adaptation, in accordance with aspects ofthe present specification;

FIGS. 4(a)-4(c) are diagrammatical representations of different datasetscorresponding to different domains for use in the system and method formemory augmented domain adaptation, in accordance with aspects of thepresent specification; and

FIGS. 5(a)-5(e) are diagrammatical representations of a comparison ofperformance of different methods domain adaptation, in accordance withaspects of the present specification.

DETAILED DESCRIPTION

The following description presents exemplary systems and methods formemory augmented domain adaptation. Particularly, embodiments describedhereinafter present exemplary systems and methods that facilitateenhanced memory augmented continuous learning for adapting amachine-learnt model to a new domain to deliver better performance witha relatively small set of samples. For example, the systems and methodsfacilitate enhanced performance of tasks such as classification andsegmentation when the machine-learnt model is deployed in a targetdomain using a small set of samples. Moreover, the systems and methodspresented hereinafter provide an elegant solution to circumventdrawbacks associated with currently available methods. In particular,the systems and methods for memory augmented domain adaptation present alearning technique to adapt a machine-learnt model to newer domains withas few samples as possible. The newer samples associated with thedeployed target domain are “remembered” so that the output generated bythe present systems and methods is constantly evolving whilecircumventing any modifications to the base.

The systems and methods entail use of a “meta-learning” techniquedesigned to enable improvements in the performance of the machine-learntmodel when deployed in new domains In particular, the system includes amemory unit which acts like a programmable memory and is used tocontinuously learn and facilitate adaptation to a target domain using asmall set of samples corresponding to the target domain Accordingly,when a similar case is subsequently encountered by the machine-learntmodel, the memory unit is queried for a match. More particularly, theadditional memory unit facilitates retrieval of samples similar to aparticular use-case and the corresponding annotations. Furthermore, theannotations corresponding to the retrieved samples stored in the memoryunit may be revised, thereby providing enhanced control over thesubsequent predictions by the machine-learnt model. It may be noted thatthe terms domain and site may be used interchangeably.

For clarity, exemplary embodiments of the present systems and methodsare described in the context of a medical imaging system. It may benoted that although the exemplary embodiments illustrated hereinafterare described in the context of a medical imaging system, other imagingsystems and applications such as industrial imaging systems andnon-destructive evaluation and inspection systems, such as pipelineinspection systems, liquid reactor inspection systems, are alsocontemplated. Some examples of the medical imaging system may include acomputed tomography (CT) system, a single photon emission computedtomography system (SPECT) system, an X-ray imaging system, a magneticresonance imaging (MRI) system, an optical imaging system, and/or anultrasound imaging system. Additionally, the exemplary embodimentsillustrated and described hereinafter may find application inmulti-modality imaging systems that employ an X-ray imaging system inconjunction with other imaging modalities, position-tracking systems orother sensor systems. In one example, the multi-modality imaging systemmay include a positron emission tomography (PET) imaging system-X-rayimaging system. Furthermore, in other non-limiting examples of themulti-modality imaging systems, the X-ray imaging system may be used inconjunction with other imaging systems, such as, but not limited to, acomputed tomography (CT) imaging system, a contrast enhanced ultrasoundimaging system, an ultrasound imaging system, an optical imaging system,a magnetic resonance (MR) imaging system and other imaging systems, inaccordance with aspects of the present specification. An exemplaryenvironment that is suitable for practicing various implementations ofthe present system and methods is discussed in the following sectionswith reference to FIG. 1.

FIG. 1 illustrates an exemplary imaging system 100 configured to receiveand process an input image corresponding to a target domaincorresponding to a target volume in a subject 102 such as a patient or anon-biological object to generate an output, where the output is usedfor further analysis. In particular, the system 100 is configured to usean exemplary memory augmented domain adaptation technique to adapt amachine-learnt model 106 to a target domain It may be noted that themachine-learnt model 106 is typically trained using a datasetcorresponding to a source domain. The system 100 is configured to adaptthe machine-learnt model 106 to the target domain using a relativelysmall set of samples corresponding to the target domain. In oneembodiment, the imaging system 100 for example, may include an X-rayimaging system, a PET system, a SPECT system, a CT imaging system, anMRI system, a hybrid imaging system, and/or a multi-modality imagingsystem.

In one embodiment, the patient 102 may be suitably positioned, forexample, on a table to allow the system 100 to image the target volumeof the patient 102. During imaging, an image acquisition device 104 thatis operatively coupled to a medical imaging system 108 may be used toacquire image data corresponding to an object or the targetvolume/region of interest in the patient 102. However, in certain otherembodiments, the input image may be retrieved from a data storage.

Additionally, the medical imaging system 108 is configured to receive aninput image or image data corresponding to the patient 102 and processthe image data to generate an output corresponding to the patient 102.In a presently contemplated configuration, the system 100 may beconfigured to acquire image data representative of the patient 102. Asnoted hereinabove, in one embodiment, the system 100 may acquire imagedata corresponding to the patient 102 via the image acquisition device104. Also, in one embodiment, the image acquisition device 104 mayinclude a probe, where the probe may include an invasive probe, or anon-invasive or external probe, such as an external ultrasound probe,that is configured to aid in the acquisition of image data. Also, incertain other embodiments, image data may be acquired via one or moresensors (not shown) that may be disposed on the patient 102 or via useof other means of acquiring image data corresponding to the patient 102.By way of example, the sensors may include physiological sensors (notshown) such as positional sensors. In certain embodiments, thepositional sensors may include electromagnetic field sensors or inertialsensors. These sensors may be operationally coupled to a dataacquisition device, such as an imaging system, via leads (not shown),for example. Other methods of acquiring image data corresponding to thepatient 102 are also contemplated.

Moreover, the medical imaging system 108 may include an acquisitionsubsystem 110 and a processing subsystem 112, in one embodiment.Further, the acquisition subsystem 110 of the medical imaging system 108is configured to acquire image data or an input image representative ofthe patient 102 via the image acquisition device 104, in one embodiment.It may be noted that the terms image, image frames, and input image maybe used interchangeably.

In addition, the acquisition subsystem 110 may also be configured toacquire images stored in an optical data storage article (not shown). Itmay be noted that the optical data storage article may be an opticalstorage medium, such as a compact disc (CD), a digital versatile disc(DVD), multi-layer structures, such as DVD-5 or DVD-9, multi-sidedstructures, such as DVD-10 or DVD-18, a high definition digitalversatile disc (HD-DVD), a Blu-ray disc, a near field optical storagedisc, a holographic storage medium, or another like volumetric opticalstorage medium, such as, for example, two-photon or multi-photonabsorption storage format. Further, the 2D images so acquired by theacquisition subsystem 110 may be stored locally on the medical imagingsystem 108 in a data repository 116, for example.

Additionally, the image data acquired from the patient 102 may then beprocessed by the processing subsystem 112. The processing subsystem 112,for example, may include one or more application-specific processors,graphical processing units, digital signal processors, microcomputers,microcontrollers, Application Specific Integrated Circuits (ASICs),Field Programmable Gate Arrays (FPGAs), Programmable Logic Arrays(PLAs), and/or other suitable processing devices. Alternatively, theprocessing subsystem 112 may be configured to store the acquired imagedata and/or the user input in a data repository 116 and/or in a memoryunit 118 for later use. In one embodiment, the data repository 116, forexample, may include a hard disk drive, a floppy disk drive, a compactdisk-read/write (CD-R/W) drive, a Digital Versatile Disc (DVD) drive, aflash drive, and/or a solid-state storage device.

It may be noted that the examples, demonstrations, and process stepsthat may be performed by certain components of the present system, forexample by the processing subsystem 112, may be implemented by suitablecode on a processor-based system. To that end, the processor-basedsystem, for example, may include a general-purpose or a special-purposecomputer. It may also be noted that different implementations of thepresent specification may perform some or all of the steps describedherein in different orders or substantially concurrently.

According to aspects of the present specification, the image dataacquired and/or processed by the medical imaging system 108 may beemployed to perform one or more tasks. In one example, the processingsubsystem 112 may include the machine-learnt model 106 such as a neuralnetwork that is configured to perform the tasks. In particular, themachine-learnt model 106 may be trained using a dataset corresponding toa source domain to perform the tasks. By way of a non-limiting example,the machine-learnt model 106 may be trained to classify the input imageand/or segment one or more regions in the input image to aid a clinicianin providing a diagnosis. In certain embodiments, the processingsubsystem 112 may be further coupled to a storage system, such as thedata repository 116, where the data repository 116 is configured tostore the acquired image data. In certain embodiments, the datarepository 116 may include a local database (not shown).

Moreover, in accordance with aspects of the present specification, theimaging system 100 may also include the memory unit 118. Although theconfiguration of FIG. 1 depicts the data repository 116 as including thememory unit 118, in other embodiments, the memory unit 118 may be astandalone unit that is external to the data repository 116 and/or theimaging system 100. The memory unit 118 is configured to store inputimages and outputs generated by the system 100.

As previously noted, the presently available techniques suffer fromdegraded performance of a machine-learnt model when the machine-learntmodel is deployed in a new target domain. In accordance with aspects ofthe present specification, the imaging system 100 is designed tocircumvent the shortcomings of the presently available techniques. Moreparticularly, imaging system 100 includes a memory augmented domainadaptation platform 114 that is configured to aid in the automatedadaptation of the machine-learnt model to a new target domain Theexemplary system 100 that includes the memory augmented domainadaptation platform 114 provides a framework for deploying themachine-learnt model 106 in the target domain by enabling themachine-learnt model 106 to adapt to the target domain using arelatively small set of images corresponding to the target domain, whichwill in turn simplifies the clinical workflow. In particular, the memoryaugmented domain adaptation platform 114 works in conjunction with themachine-learnt model 106 to enhance the adaptability of themachine-learnt model 106 to the target domain, and thereby improve theperformance of the imaging system 100. It may be noted that the terms“new domain,” “target site,” and “target domain” may be usedinterchangeably.

Also, in the presently contemplated configuration illustrated in FIG. 1,the processing subsystem 112 is shown as including the memory augmenteddomain adaptation platform 114. However, in certain embodiments, thememory augmented domain adaptation platform 114 may also be used as astandalone unit that is physically separate from the processingsubsystem 112 and the medical imaging system 108. By way of example, thememory augmented domain adaptation platform 114 may be external to andoperatively coupled to the medical imaging system 108.

The exemplary memory augmented domain adaptation platform 114 isconfigured to circumvent the shortcomings of the presently availabledomain adaptation techniques. More particularly, the memory augmenteddomain adaptation platform 114 is configured to facilitate adaptation ofthe machine-learnt model to a target domain using a small set of sampleimages corresponding to the target domain, thereby leading to consistentoutcomes.

As previously noted, a given model is typically trained using thedataset corresponding to the source domain. It is desirable that themachine-learnt model 106, when deployed in the target domain adapts tothe target domain, while maintaining the performance of the system 100.Accordingly, when the imaging system 100 is deployed in the targetdomain, the memory augmented domain adaptation platform 114 isconfigured to adapt the machine-learnt model 106 to the target domain toperform a given task. By way of example, the memory augmented domainadaptation platform 114 may be configured to aid the machine-learntmodel in processing the acquired input image to classify the input imageand/or segment one or more regions of interest in the input image.

Accordingly, in operation, the machine-learnt model 106 and/or thememory augmented domain adaptation platform 114 are configured toreceive an input image, where the input image corresponds to the targetdomain Further, the memory augmented domain adaptation platform 114 isconfigured to compute one or more features corresponding to the inputimage. The features include shape features, texture features, and thelike. In certain other embodiments, other features may be used toidentify the set of support images. Some non-limiting examples of theother features include age of the patient 102, gender of the patient102, electronic medical record (EMR) information corresponding to thepatient 102, demography, and the like.

It may be noted that the memory unit 118 is configured to store one ormore images corresponding to the target domain Additionally, the memoryunit 118 may also be configured to store one or more features such astexture features and/or shape features and masks corresponding to theimages of the target domain. Moreover, other features corresponding tothe images of the target domain such as age, gender, EMR information,demography, and the like may be stored.

Moreover, subsequent to receipt of the input image, the memory augmenteddomain adaptation platform 114 is configured to query the datarepository 116 and the memory unit 118 in particular to identify amatching set of images based on the features of the input image. In oneexample, consequent to the query, the memory augmented domain adaptationplatform 114 may identify a set of support images in the memory unit 118based on the image features corresponding to the input image. It may benoted that in accordance with aspects of the present specification, incertain embodiments, the system 100 may be configured to allow aclinician to identify the set of support images.

It may be noted that the set of support images corresponds to the targetdomain and may be a subset of images corresponding to the target domain.In one non-limiting example, the set of support images includes imagesin a range from about three images to about five images. However, use ofother number of images in the set of support images is alsocontemplated. More particularly, a small subset of the target domainimages may be used as the set of support images. Use of the small subsetof the target domain images as the set of support images aids incircumventing the need for use of a large pool/set of training data bythe currently available techniques.

Traditionally, the currently available techniques provide only the inputimage to the machine-learnt model to perform a given task. In accordancewith aspects of the present specification, the memory augmented domainadaptation platform 114 is configured to augment an input to themachine-learnt model 106 with the set of support images. In particular,the memory augmented domain adaptation platform 114 is configured tocompute a set of features and/or a set of masks corresponding to the setof support images and provide the set of features and/or set of masks asadditional input to the machine-learnt model 106 to adapt themachine-learnt model 106 to the target domain. Consequently, themachine-learnt model 106 “adapts” or “learns” the target domain usingthe small set of support images.

Moreover, the memory augmented domain adaptation platform 114 aids inthe generation of an output by the machine-learnt model 106 based atleast on the set of features and/or set of masks corresponding to theset of support images. Furthermore, the memory augmented domainadaptation platform 114 is configured to provide the output tofacilitate analysis. Also, the output generated may be based on the taskperformed by the machine-learnt model 106. For example, if themachine-learnt model 106 is configured to classify the input image, theoutput may be a binary value. However, if the machine-learnt model 106is configured to segment the input image, the output may be an imagecorresponding to the segmented region(s) of interest. Moreover, in oneexample, the output may be visualized on an interface unit such as adisplay 120.

Furthermore, as illustrated in FIG. 1, the medical imaging system 108may include the display 120 and a user interface 122. In certainembodiments, such as in a touch screen, the display 120 and the userinterface 122 may overlap. Also, in some embodiments, the display 120and the user interface 122 may include a common area. In accordance withaspects of the present specification, the display 120 of the medicalimaging system 108 may be configured to display or present the outputgenerated by the machine-learnt model 106. Moreover, any qualitymetrics/indicators generated by the memory augmented domain adaptationplatform 114 may also be visualized on the display 120.

In addition, the user interface 122 of the medical imaging system 108may include a human interface device (not shown) configured to aid theclinician in manipulating image data displayed on the display 120. Thehuman interface device may include a mouse-type device, a trackball, ajoystick, a stylus, or a touch screen configured to facilitate theclinician to identify the one or more regions of interest in the images.However, as will be appreciated, other human interface devices, such as,but not limited to, a touch screen, may also be employed. Furthermore,in accordance with aspects of the present specification, the userinterface 122 may be configured to aid the clinician in navigatingthrough the acquired images and/or output generated by the medicalimaging system 108. Additionally, the user interface 122 may also beconfigured to aid in manipulating and/or organizing the displayed imagesand/or generated indicators displayed on the display 120.

Implementing the imaging system 100 that includes the memory augmenteddomain adaptation platform 114 as described hereinabove aids inenhancing the performance of the machine-learnt model 106 when the model106 is deployed in a new target domain In particular, the memoryaugmented domain adaptation platform 114 aids in facilitating theadaptation of the machine-learnt model 106 to the target domain via useof the small set of support images. Additionally, the memory augmenteddomain adaptation platform 114 provides continuous learning to themachine-learnt model 106 via use of the set of support images, therebyimproving the performance of the machine-learnt model 106 when the model106 is deployed in new target domains.

In the present specification, embodiments of exemplary methods of FIGS.2-3 may be described in a general context of computer executableinstructions on a computing system or a processor. Generally, computerexecutable instructions may include routines, programs, objects,components, data structures, procedures, modules, functions, and thelike that perform particular functions or implement particular abstractdata types.

Additionally, embodiments of the exemplary methods of FIGS. 2-3 may alsobe practiced in a distributed computing environment where optimizationfunctions are performed by remote processing devices that are linkedthrough a wired and/or wireless communication network. In thedistributed computing environment, the computer executable instructionsmay be located in both local and remote computer storage media,including memory storage devices.

Further, in FIGS. 2-3, the exemplary methods are illustrated as acollection of blocks in a logical flow chart, which representsoperations that may be implemented in hardware, software, orcombinations thereof. The various operations are depicted in the blocksto illustrate the functions that are performed. In the context ofsoftware, the blocks represent computer instructions that, when executedby one or more processing subsystems, perform the recited operations.

The order in which the exemplary methods of FIGS. 2-3 are described isnot intended to be construed as a limitation, and any number of thedescribed blocks may be combined in any order to implement the exemplarymethods disclosed herein, or equivalent alternative methods.Additionally, certain blocks may be deleted from the exemplary methodsor augmented by additional blocks with added functionality withoutdeparting from the spirit and scope of the subject matter describedherein. Although, the exemplary embodiments illustrated hereinafter aredescribed in the context of a medical imaging system, it will beappreciated that use of the systems and methods in industrialapplications is also contemplated in conjunction with the presentspecification.

Referring now to FIG. 2, a flow chart 200 depicting an exemplary methodfor adapting a machine-learnt model to a target domain is presented. Themethod 200 of FIG. 2 is described with reference to the components ofFIG. 1. In one embodiment, the method 200 may be performed by the memoryaugmented domain adaptation platform 114 in conjunction with themachine-learnt model 106.

The method includes receiving an input image, when the imaging system100 and the machine-learnt model 106 in particular is deployed in thetarget domain, as indicated by step 202. The input image corresponds toa target domain Also, the input image may be received by themachine-learnt model 106 and the memory augmented domain adaptationplatform 114.

Further, at step 204, one or more features corresponding to the inputimage are computed, by the memory augmented domain adaptation platform114. These features may include texture features, shape features, or acombination thereof. Some non-limiting examples of the texture featuresinclude wavelet features, machine-learnt features and the like. Also,some non-limiting examples of the shape features include contour-basedfeatures, features derived from dictionary-based approaches, moments,shape representations such as area, tangent angles, contour curvature,shape transform domain features such as Fourier transforms, and thelike. Also, as previously noted, some examples of other features includeage, gender, EMR information of the patient, and the like.

It may be noted that images corresponding to the target domain may bestored in the memory unit 118. Subsequent to the computation of theimage features corresponding to the input image, a set of support imagesmay be identified by the memory augmented domain adaptation platform 114based on the image features of the input image, as indicated by step206. In particular, the memory augmented domain adaptation platform 114is configured to query the memory unit 118 using the image featurescorresponding to the input image to identify the set of support imagesfrom the images stored in the memory unit 118. It may be noted that theset of support images is a subset of the images corresponding to thetarget domain In one non-limiting example, the set of support imagesincludes images in a range from about three images to about five images.Further, in a scenario where the query to the memory unit 118 fails toidentify one or more support images, one or more images corresponding tothe source domain may be used as the support images.

Conventional machine-learning techniques generate an output based solelyon the received input image, thereby leading to degradation ofperformance of the machine-learnt model in a new domain. In accordancewith aspects of the present specification, the shortcomings of thepresently available techniques are circumvented via use of the retrievedset of support images. The set of support images is used bymachine-learnt model 106 to provide a context for the prediction asopposed to using only the input image.

More particularly, the method includes augmenting an input to themachine-learnt model with a set of features and/or set of maskscorresponding to the set of support images to adapt the machine-learntmodel to the target domain, as depicted by step 208. Accordingly, thememory augmented domain adaptation platform 114 is configured to computeone or more features and/or masks corresponding to the retrieved set ofsupport images. In one example, a tunable feature extraction suited forsegmentation may be used to compute the features corresponding to theset of support images. In another example, a support context vectoraugmentation may be used to compute the features corresponding to theset of support images. In yet another example, the featurescorresponding to the set of support images may be computed by mimickingsettings corresponding to a target domain during the training phase ofthe machine-learnt model 106 with data corresponding to the sourcedomain.

As previously noted, these features may include texture features and/orshape features corresponding to the set of support images. Subsequently,the memory augmented domain adaptation platform 114 provides thefeatures and/or masks corresponding to the set of support images asadditional input to a predictor of the machine-learnt model 106, therebyaugmenting the input to the machine-learnt model 106. Moreover, otherfeatures may also be provided as additional input to the predictor ofthe machine-learnt model 106, as previously noted.

Furthermore, at step 210, the machine-learnt model 106 is configured togenerate an output based at least on the set of features and/or set ofmasks corresponding to the set of support images provided by the memoryaugmented domain adaptation platform 114 and the input image. The outputgenerated by the machine-learnt model 106 may vary based on the taskperformed by the machine-learnt model 106. By way of example, if themachine-learnt model 106 is used to perform a classification task, theoutput generated by the machine-learnt model 106 may be a binary value.In a similar fashion, if the machine-learnt model 106 is used to performa segmentation task, the output generated by the machine-learnt model106 may be a mask or a segmented image.

Furthermore, the output may be utilized to facilitate analysis, asindicated by step 212. By way of example, the memory augmented domainadaptation platform 114 may be configured to visualize the mask orsegmented image and/or the binary value generated by the machine-learntmodel 106 on the display 120. In certain embodiments, a visualcomparison the performance of the system 100 with and without domainadaptation may be visualized on the display 120 to aid the clinician inany diagnosis or analysis. Additionally, any metrics associated with theoutput may also be visualized on the display 120. In certainembodiments, the metrics may be superimposed on a corresponding outputon the display 120.

In another example, the memory augmented domain adaptation platform 114may also be configured to communicate the generated output to a usersuch as a clinician or another system. The clinician and/or anothersystem may use the output to facilitate a diagnosis and/or an analysis.The method 200 will be described in greater detail with reference toFIG. 3.

Turning now to FIG. 3, a schematic representation 300 illustrating theexemplary method for memory augmented domain adaptation of FIG. 2 isdepicted. Also, FIG. 3 is described with reference to the components ofFIG. 1-2.

As previously noted, the machine-learnt model 106 is typically trainedusing a dataset corresponding to a source domain. Once themachine-learnt model 106 is deployed in a new target domain, theexemplary memory augmented domain adaptation platform 114 is configuredto adapt the machine-learnt model 106 to the target domain using arelatively small set of support images. In one non-limiting example, theset of support images may include images in a range from about threeimages to about five images.

Further, as depicted in FIG. 3, an input image 302 corresponding to atarget domain is received by a machine-learnt model 106 and the memoryaugmented domain adaptation platform 114. Subsequently, the memoryaugmented domain adaptation platform 114 computes one or more features304 corresponding to the input image 302. These features include texturefeatures, shape features, and/or other features.

Moreover, the memory augmented domain adaptation platform 114 isconfigured to identify a set of support images corresponding to thetarget domain using the computed features associated with the inputimage 302. Accordingly, the memory augmented domain adaptation platform114 is configured to query the memory unit 118 based on the featurescorresponding to the input image 302 to identify existence of a match inthe memory unit 118. Specifically, the memory augmented domainadaptation platform 114 is configured to identify a set of supportimages based on the one or more image features corresponding to theinput image 302. The set of support images corresponds to the targetdomain and include features that match the features of the input image302.

It may be noted that in a classical U-Net, given training pairs ofimages and segmentation masks {I_(k), S_(k)}, k=1, 2, . . . , N, aframework learns a predictor

[·π defined by parameters w that minimizes a training loss. One suchexample is presented in equation (1).

$\begin{matrix}{{RMSE} = {\frac{1}{N}{\sum_{k = 1}^{N}{{S_{x} - {\lbrack I_{k} \rbrack}}}^{2}}}} & (1)\end{matrix}$

where

[·] is a learnt predictor and is a composition of an encoder and decoderD_(w)·E_(w).

In accordance with aspects of the present specification, a memory unit Msuch as the memory unit 118 is provided. The memory unit M 118 isdefined by a matrix T_(N×Ft), where F_(t) is a feature lengthrepresenting the texture features and a matrix G_(N×Fs), where F_(s) isa feature length representing the shape features. In one example, thememory unit M 118 is defined as:

M=(T _(N×Ft) , G _(N×Fs))   (2)

In response to the query, for every input image I_(k), the memory unit M118 is configured to return a set of support images S(I_(k)) 306. Oneexample of the set of support images S(I_(k)) 306 is presented inequation (3).

S(I _(k))=p _(t), {t=1, 2, . . . , T}  (3)

In one example, in equation (1), T is a constant and is representativeof a number of images in the set of support images S(I_(k)) 306. Aspreviously noted, the set of support images S(I_(k)) 306 corresponds tothe target domain and is a subset of images corresponding to the targetdomain. In one non-limiting example, the set of support images includesimages in a range from about three images to about five images. However,use of other number of images in the set of support images is alsocontemplated.

For a given image I_(k), the set of support images S(I_(k)) 306 iscomputed as:

(s ₁ , s ₂ , . . . , s _(T))=NN_(T)(q(I_(k)), M)   (4)

where q is a feature corresponding to the input image I_(k) and NN_(T)are the T nearest neighbors for the given input q(I_(k)).

The nearest neighbor operator NN is defined as:

A=NN(I _(k) , M)=argmax_(i) q(I _(k))·q(M _(i))   (5)

The memory unit 118 functions like a programmable memory and is used tofacilitate adaptation and continuous learning of the machine-learntmodel 106 to the target domain using a small set of support imagescorresponding to the target domain. Hence, when a similar case issubsequently encountered by the machine-learnt model 106, the memoryunit 106 is queried for a match. More particularly, the additionalmemory unit 118 facilitates retrieval of support images/samples similarto a particular use-case and the corresponding annotations.

In addition, the memory augmented domain adaptation platform 114 isconfigured to augment an input to the machine-learnt model 106 with theset of support images S(I_(k)) 306. The set of support images S(I_(k))306 is used by machine-learnt model 106 to provide a context for theprediction as opposed to using only the input image. More particularly,the memory augmented domain adaptation platform 114 is configured tocompute a set of features and/or set of masks 308 corresponding to theset of support images S(I_(k)) 306. As previously noted with referenceto FIG. 2, different methods may be used to compute the features/masks308 corresponding to the set of support images S(I_(k)) 308.

Further, the memory augmented domain adaptation platform 114 isconfigured to provide the set of features and/or set of masks 308corresponding to the set of support images S(I_(k)) 306 as additionalinput to the machine-learnt model 106 to adapt the machine-learnt model106 to the target domain More particularly, in the machine-learnt model106 (for example, a support augmented neural network), the input to apredictor {circumflex over (F)} of the machine-learnt model 106 isaugmented with a set of texture and/or shape features and set of masks308 corresponding to the set of support images S(I_(k)) 306. Moreover,the set of support images S(I_(k)) 306 is used by the machine-learntmodel 106 to provide a context for the prediction as opposed to usingonly the input image 302. The texture and shape features are defined by:

Texture Features T: I_(k)→T_(Ft×1)   (6)

Shape Features G: S_(k)→T_(Fs×1)   (7)

More particularly, the input to the decoder of the predictor F ischanged to be a combination of the learnt encoded input E(I_(k)) and theshape and/or texture features and/or masks 308 corresponding to the setof support images S(I_(k)) 306. By way of example, the memory augmenteddomain adaptation platform 114 is configured to provide as input to themachine-learnt model 106 one or more machine-learnt features, one ormore hard-coded features, masks 308 corresponding to the set of supportimages S(I_(k)) 306, or combinations thereof.

Additionally, the machine-learnt model 106 is configured to perform adesired task, such as but not limited to, a segmentation task and aclassification task. Accordingly, the machine-learnt model 106 isconfigured to generate a modified output 310 based at least on the setof features and/or set of masks 308 corresponding to the set of supportimages S(I_(k)) 306. In one example, the output 310 includes a segmentedimage/mask, a binary value, or a combination thereof 312. Also, in oneexample, the modified output 310 generated by the machine-learnt model106 is represented as:

=D·(E[I _(k)

T[I _(k) ]

G[I _(k)])   (8)

where

is an operator such as, but not limited to, concatenation, average, sum,and the like and is used to combine the different features and/or masks.

The masks/images and/or binary values 312 generated as output by themachine-learnt model 106 are communicated to the memory augmented domainadaptation platform 114). Additionally, the memory augmented domainadaptation platform 114 is configured to facilitate continuous learningof the machine-learnt model 106. Accordingly, the memory augmenteddomain adaptation platform 114 is configured to verify validity of theoutput 310 (step 314). At step 314, if it is verified that the output310 is valid, the memory unit 118 is updated to store the output 310(step 316). In particular, at step 316, one or more features and/or oneor more masks 320 corresponding to the set of support images S(I_(k))306 are stored in the memory unit 118. It may be noted that the featuresand/or masks 320 are tuned for performing tasks such as, but not limitedto classification and segmentation.

However, at step 314, if the validity of the output 310 is not verified,one or more annotations corresponding to the set of support imagesS(I_(k)) 306 may be revised to generate a set of revised support images(step 318). Subsequently, the memory unit 118 is updated with the set ofrevised support images and features and/or masks corresponding to theset of revised support images. This validation and updating of thememory unit 118 aids in facilitating the continuous learning of themachine-learnt model 106, thereby enhancing the domain adaptationcapability of the machine learnt model 106.

In certain embodiments, the memory augmented domain adaptation platform114 may also be configured to update the memory unit 118 to optimize thememory unit M 118. By way of example, the memory augmented domainadaptation platform 114 may delete one or more support images of the setof support images S(I_(k)) 306 based on relevance of the set of supportimages S(I_(k)) 306 to optimize the memory unit M 118, while enablingenhanced performance of the imaging system 100. By way of example, totrack the relevance of a support image 306, the memory augmented domainadaptation platform 114 may be configured to monitor the number of timesthat support image 306 is used for prediction, check the age of thesupport image 306 in the memory unit 118, determine the similarity ofthe support image 306 to other images in the memory unit 118, and thelike.

Furthermore, the memory augmented domain adaptation platform 114 isconfigured to provide or communicate the output 310 to facilitateanalysis (step 322). In one non-limiting example, the output 310 may bevisualized on an interface unit such as the display 120. The output 310may be used for providing a diagnosis or for further analysis.

Implementing the memory augmented domain adaptation platform 114 asdescribed hereinabove aids in adapting the machine-learnt model 106 tothe target domain while circumventing the need for retraining themachine-learnt model 106. In addition, since the machine-learnt model106 is adapted to the target domain using a small set of support images306, the need for a voluminous dataset corresponding to the targetdomain to retrain the machine-learnt model 106 is obviated. Moreover,the machine-learnt model 106 is continuously trained using the set ofsupport images 306 that is stored in the memory unit 118 and provided bythe memory augmented domain adaptation platform 114, thereby furtherenhancing the adaptation of the machine-learnt model 106 to the targetdomain.

Referring now to FIGS. 4(a)-4(c), diagrammatical representations ofdifferent datasets 402, 404, 406 corresponding to different domains arepresented. In particular, these datasets 402, 404, 406 correspond todifferent domains with subjects having different diseases to simulate adeployment scenario of a machine-learnt model. Also, FIGS. 4(a)-4(c) aredescribed with reference to the components of FIG. 1-3. It may be notedthat in the present example, the datasets 402, 404, 406 include X-rayimages corresponding to three different domains. Additionally, thesamples such as the X-ray images corresponding to the three datasets402, 404, 406 have variations in texture, disease conditions, andgender. In the example depicted in FIGS. 4(a)-4(c), use of themachine-learnt model to perform a lung segmentation task from X-rayimages is represented. As will be appreciated, lung segmentation isconsidered a challenging task owing to variations in the lung due tovariations in anatomy, diseases, and the like amongst differentpatients. The three datasets 402, 404, 406 are used to understand theeffect of changes in domains on the performance of the machine-learntmodel. A U-Net is employed as a base learning model.

FIG. 4(a) depicts a first dataset 402. In the present example, the firstdataset 402 is a Montgomery TB dataset. The Montgomery TB dataset 402 isan open source NHS dataset and includes 138 posterior-anterior X-rayimages. Of these images, 80 X-ray images are indicative of normal dataand 58 X-ray images are representative of abnormal data withmanifestations of tuberculosis. Moreover, in the present example, theMontgomery TB dataset 402 is a source dataset used to train themachine-learnt model 106.

Further, FIG. 4(b) depicts a second dataset 404. The second dataset 404is a GE pneumoconiosis dataset. The GE pneumoconiosis dataset 404includes 330 images with lung mask annotations. It may be noted thatpneumoconiosis is an occupational lung disease that typically affectsfactory workers and early stages of the diseases can be detected by dustand other metal settlements in the lungs. In the present example, the GEpneumoconiosis dataset 404 corresponds to a first target domain.

Moreover, FIG. 4(c) illustrates a third dataset 406. The third dataset406 is a Japanese Society of Radiological Technology (JSRT) dataset. TheJSRT dataset 406 includes chest X-rays with lung nodules. Also, the JSRTdataset 406 includes 247 images. In the present example, the JSRTdataset 406 corresponds to a second target domain

In addition, to demonstrate the effectiveness of use of the memoryaugmented domain adaptation platform 114, the machine-learnt model 106is trained using samples obtained exclusively from the Montgomery TBdataset 402. Once the machine-learnt model 106 is trained using samplesfrom the Montgomery TB dataset 402, the machine-learnt model 106 isdeployed in new target domains to test the adaptability of themachine-learnt model 106 to the target domains using samples fromcorresponding target domains In one example, the trained machine-learntmodel 106 is deployed in new target domains and the domain adaptation ofthe machine-learnt model 106 is tested using samples corresponding tothe GE pneumoconiosis dataset 404 and the JSRT dataset 406. The resultsare validated using a Dice score.

Table 1 presents a comparison of domain adaptation performance ofvarious machine-learnt models using the datasets 402, 404, 406 of FIGS.4a )-4(c). Column 1 of Table 1 lists the techniques used in thecomparative study. Further, column 2 of Table 1 corresponds to thesource dataset 402 (Montgomery TB dataset). Also, column 3 of Table 1corresponds to the first target dataset 404 (GE pneumoconiosis dataset).Column 4 of Table 1 corresponds to the second target dataset 406 (JSRTdataset). Moreover, row 1 of Table 1 corresponds to the performance of abase technique or learning model (U-Net) in the three domains 402, 404,406. Similarly, a second row of Table 1 of corresponds to theperformance of the method for adapting the machine-learnt model 106(SupportNet) described hereinabove in the three domains 402, 404, 406.It may be noted that for the results presented in in row 2 of Table 1,the augmented input provided by the memory augmented domain adaptationplatform 114 to the machine-learnt model 106 includes only the set offeatures corresponding to the set of support images. Additionally, row 3of Table 1 corresponds to the performance of the present method(SupportNet) in the three domains 402, 404, 406. It may be noted that inrow 3 of Table 1, the augmented input provided by the memory augmenteddomain adaptation platform 114 to the machine-learnt model 106 includesthe set of features and/or set of masks corresponding to the set ofsupport images.

TABLE 1 Technique Montgomery Pneumoconiosis JSRT U-Net 0.968 0.882 0.896SupportNet using a support 0.958 0.910 0.918 set of features SupportNetusing a support 0.959 0.947 0.964 set of features and masks

Moreover, the results presented in Table 1 are obtained using a Dicescore of 1 as a metric. Based on the results presented in Table 1, itmay be deduced that the performance of the SupportNet (see rows 2 and 3)that uses the augmented input of support features and support masks isbetter than the performance of the U-Net (see row 1) on images obtainedfrom different cohorts/domains. Accordingly, the SupportNet enjoysbetter generalization than the U-Net.

Turning now to FIGS. 5(a)-5(e), diagrammatic illustrations of acomparison of results of the domain adaptation performance of differentmachine-learnt models presented in Table 1 to perform a desired task aredepicted. In the example presented in FIGS. 5(a)-5(e), it desirable touse the machine-learnt model 106 to segment the lung region in the inputimage 502. Also, FIGS. 5(a)-5(e) are described with reference to thecomponents of FIGS. 1-4.

FIG. 5(a) represents an input image 502 such as the input image 302 ofFIG. 3. Further, FIG. 5(b) represents a ground truth mask 504. In oneexample, the image 504 may include ground truth annotation by aclinician.

Also, FIG. 5(c) represents a segmented image 506 generated using abaseline technique such as the U-net without domain adaptation.Similarly, FIG. 5(d) represents a segmented image 508 generated by theSupportNet using only features corresponding to a set of support images.Also, FIG. 5(e) represents a segmented image 510 generated by theSupportNet using features and masks corresponding to a set of supportimages.

It may be noted that using only the features from the set of supportimages corresponding to the nearest neighbors aids the SupportNet with asuitable feature prior. In addition, when the features from the set ofsupport images are augmented with the masks from the set of supportimages, the machine-learnt model achieves the dual objectives ofbalancing the shape as well as providing high fidelity to the images.

Embodiments of the present systems and methods for memory augmenteddomain adaptation advantageously present a continuous learning-basedtechnique to adapt a machine-learnt model to a target domain to deliverbetter performance with a small set of samples. Moreover, the outputgenerated by present systems and methods facilitates enhancedunderstanding of the predictions by comparing a set of similar examplesused for arriving at a decision. Moreover, the systems and methodsenable domain adaptation and continuous learning of the machine-learntmodel without the constraint of requiring a large dataset of targetdomain samples during a development phase of the technique. Inparticular, the systems and methods enable domain adaptation with a verysmall set of samples.

Additionally, the systems and methods entail use of a “meta-learning”technique designed to enable improvements in the performance of themachine-learnt model when deployed in new domains. In particular, thememory unit is used to facilitate adaptation and continuous learning ofthe machine-learnt model to a target domain using a small set of samplescorresponding to the target domain. Accordingly, when a similar case issubsequently encountered by the machine-learnt model, the memory unit isqueried for a match. More particularly, the additional memory unitfacilitates retrieval of samples similar to a particular use-case andthe corresponding annotations. Furthermore, the annotationscorresponding to the retrieved samples stored in the memory unit may berevised, thereby providing enhanced control over the subsequentpredictions by the machine-learnt model.

It may be noted that the foregoing examples, demonstrations, and processsteps that may be performed by certain components of the presentsystems, for example by the processing subsystem 112 and the memoryaugmented domain adaptation platform 114 in particular, may beimplemented by suitable code on a processor-based system. Theprocessor-based system, for example, may include a general-purpose or aspecial-purpose computer. It may also be noted that differentimplementations of the present specification may perform some or all ofthe steps described herein in different orders or substantiallyconcurrently.

Additionally, the functions may be implemented in a variety ofprogramming languages, including but not limited to Ruby, HypertextPreprocessor (PHP), Perl, Delphi, Python, C, C++, or Java. Such code maybe stored or adapted for storage on one or more tangible,machine-readable media, such as on data repository chips, local orremote hard disks, optical disks (that is, CDs or DVDs), solid-statedrives, or other media, which may be accessed by the processor-basedsystem to execute the stored code.

Although specific features of embodiments of the present specificationmay be shown in and/or described with respect to some drawings and notin others, this is for convenience only. It is to be understood that thedescribed features, structures, and/or characteristics may be combinedand/or used interchangeably in any suitable manner in the variousembodiments.

While only certain features of the present disclosure have beenillustrated and described herein, many modifications and changes willoccur to those skilled in the art. It is, therefore, to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit of the invention.

1. A system, comprising: an acquisition subsystem configured to obtainimages corresponding to a target domain; a processing subsystem inoperative association with the acquisition subsystem and comprising amemory augmented domain adaptation platform, wherein the memoryaugmented domain adaptation platform is configured to: compute one ormore features corresponding to an input image, wherein the input imagecorresponds to a target domain; identify a set of support images basedon the one or more features corresponding to the input image, whereinthe set of support images corresponds to the target domain; augment aninput to a machine-learnt model with a set of features, a set of masks,or both the set of features and the set of masks corresponding to theset of support images to adapt the machine-learnt model to the targetdomain; generate an output based at least on the set of features, theset of masks, or both the set of features and the set of maskscorresponding to the set of support images; and an interface unitconfigured to present the output for analysis.
 2. The system of claim 1,wherein the machine-learnt model is configured to at least performclassification, segmentation, or both classification and segmentation ofthe input image.
 3. The system of claim 1, wherein the one or morefeatures corresponding to the input image and the set of featurescorresponding to the set of support images comprise shape features,texture features, or both shape features and texture features.
 4. Thesystem of claim 1, wherein the memory augmented domain adaptationplatform is configured to store one or more features, one or more masks,or both the one or more features and the one or more masks correspondingto the set of support images, and wherein the one or more features, oneor more masks, or both the one or more features and the one or moremasks are tuned for performing classification, segmentation, or bothclassification and segmentation in a memory unit.
 5. The system of claim1, wherein to identify the set of support images, the memory augmenteddomain adaptation platform is configured to query a memory unit todetermine the set of support images based on the one or more featurescorresponding to the input image.
 6. The system of claim 1, wherein toaugment the input to the machine-learnt model, the memory augmenteddomain adaptation platform is configured to provide as input to themachine-learnt model one or more machine-learnt features, one or morehard-coded features, one or more masks corresponding to the set ofsupport images, or combinations thereof.
 7. The system of claim 1,wherein to generate the output, the memory augmented domain adaptationplatform is configured to generate a mask, generate a binary value, or acombination thereof.
 8. The system of claim 1, wherein the memoryaugmented domain adaptation platform is further configured to: verifyvalidity of the output; revise one or more annotations corresponding tothe set of support images based on the validity of the output togenerate a set of revised support images; and update the memory unit bystoring the set of revised support images and features corresponding tothe set of revised images.
 9. The system of claim 1, the memoryaugmented domain adaptation platform is further configured to adapt themachine-learnt model to the target domain without retraining themachine-learnt model.
 10. A processing system for adapting amachine-learnt model, comprising: a memory augmented domain adaptationplatform configured to: compute one or more features corresponding to aninput image, wherein the input image corresponds to a target domain;identify a set of support images based on the one or more featurescorresponding to the input image, wherein the set of support imagescorresponds to the target domain; augment an input to the machine-learntmodel with a set of features, a set of masks, or both the set offeatures and the set of masks corresponding to the set of support imagesto adapt the machine-learnt model to the target domain; generate anoutput based at least on the set of features, the set of masks, or boththe set of features and the set of masks corresponding to the set ofsupport images; and provide the output to facilitate analysis.
 11. Amethod for adapting a machine-learnt model, the method comprising:receiving an input image, wherein the input image corresponds to atarget domain; computing one or more features corresponding to the inputimage; identifying a set of support images based on the one or morefeatures corresponding to the input image, wherein the set of supportimages corresponds to the target domain; augmenting an input to themachine-learnt model with a set of features, a set of masks, or both theset of features and the set of masks corresponding to the set of supportimages to adapt the machine-learnt model to the target domain;generating an output based at least on the set of features, the set ofmasks, or both the set of features and the set of masks corresponding tothe set of support images; and providing the output to facilitateanalysis.
 12. The method of claim 11, further comprising storing one ormore features and one or more masks corresponding to the set of supportimages, the output, or combinations thereof, wherein the one or morefeatures, the one or more masks, or both the one or more features andthe one or more masks are tuned for performing classification,segmentation, or both classification and segmentation in a memory unit.13. The method of claim 11, wherein identifying the set of supportimages comprises querying a memory unit to determine the set of supportimages based at least on the one or more features corresponding to theinput image.
 14. The method of claim 11, wherein augmenting the input tothe machine-learnt model comprises: computing the one or moremachine-learnt features, the one or more hard-coded features, or boththe one or more machine-learnt features and the one or more hard-codedfeatures corresponding to the set of support images; and providing asinput to the machine-learnt model one or more machine-learnt featurescorresponding to the set of support images, one or more hard-codedfeatures corresponding to the set of support images, one or more maskscorresponding to the set of support images, or combinations thereof. 15.The method of claim 11, wherein generating the output comprisesgenerating a mask, generating a binary value, or a combination thereof.16. The method of claim 11, further comprising: verifying validity ofthe output; revising one or more annotations corresponding to the set ofsupport images based on the validity of the output to generate a set ofrevised support images; and updating the memory unit by storing the setof revised support images, features and masks, corresponding to the setof revised support images or combinations thereof.
 17. The method ofclaim 16, further comprising updating the memory unit by deleting one ormore of the set of support images based on relevance of the set ofsupport images.
 18. The method of claim 11, wherein the set of supportimages comprises a subset of images corresponding to the target domain.19. The method of claim 11, further comprising adapting themachine-learnt model to the target domain without retraining themachine-learnt model.
 20. The method of claim 11, wherein providing theoutput comprises presenting the output on an interface unit